JP7813348B2 - Driving ability assessment system and driving ability assessment method - Google Patents

Description

The present invention relates to a driving ability assessment system and a driving ability assessment method for assessing the driving ability of a vehicle driver.

A known example of this type of device is one that measures a driver's safe driving ability (see, for example, Patent Document 1). The device described in Patent Document 1 intermittently applies a load to the driver through audio output to distract them, calculates steering entropy values that represent steering error under both a loaded and unloaded condition, and evaluates the driver's safe driving ability based on the difference between the error evaluation values calculated under the loaded and unloaded conditions.

JP 2014-174848 A

However, the device described in Patent Document 1 above requires imposing a load on the driver in order to evaluate the driver's safe driving ability, which interferes with driving.

A driving ability assessment system according to one aspect of the present invention comprises an information acquisition unit that acquires time-series driving data of a vehicle; an evaluation value calculation unit that calculates an evaluation value representing the steering characteristics of the vehicle driver based on the driving data acquired by the information acquisition unit; a driving data extraction unit that extracts first driving data of no-load/low-load sections other than high-load sections where the driving load placed on the vehicle driver due to driving behavior is high, and extracts second driving data of right-turn sections, based on the driving data acquired by the information acquisition unit; and an event occurrence determination unit that determines whether a specified event has occurred that places a specified load on the vehicle driver, based on the driving data acquired by the information acquisition unit. The evaluation value calculation unit identifies, from the driving data acquired by the information acquisition unit, driving data acquired by the information acquisition unit after the point at which the event occurrence determination unit determines that a specified event has occurred as specific driving data , and calculates a first evaluation value representing the steering characteristics of the vehicle driver based on driving data obtained by excluding the specific driving data from the first driving data, or based on driving data corrected so that the weight assigned to the evaluation value of the specific driving data included in the first driving data is lower than the weight assigned to the evaluation value of the other first driving data, and calculates a second evaluation value representing the steering characteristics of the driver when a specified load is applied to the vehicle driver based on the calculated first evaluation value and the second driving data.

Another aspect of the present invention is a driving ability assessment method, which includes an information acquisition step of acquiring time-series driving data of a vehicle, an evaluation value calculation step of calculating an evaluation value representing the steering characteristics of the vehicle driver based on the driving data acquired in the information acquisition step, a driving data extraction unit that extracts first driving data of no-load/low-load sections other than high-load sections where the driving load placed on the vehicle driver due to driving behavior is high, and extracts second driving data of right-turn sections, based on the driving data acquired in the information acquisition step, and an event occurrence assessment step of determining whether a specified event has occurred that imposes a specified load on the vehicle driver, based on the driving data acquired in the information acquisition step. In the evaluation value calculation step, of the driving data acquired in the information acquisition step, driving data acquired in the information acquisition step after the point at which it is determined that a specified event has occurred in the event occurrence determination step is identified as specific driving data , and a first evaluation value representing the steering characteristics of the vehicle driver is calculated based on driving data obtained by excluding the specific driving data from the first driving data, or based on driving data corrected so that the weight assigned to the evaluation value of the specific driving data included in the first driving data is lower than the weight assigned to the evaluation value of the other first driving data , and a second evaluation value representing the steering characteristics of the driver when a specified load is applied to the vehicle driver is calculated based on the calculated first evaluation value and the second driving data.

The present invention makes it possible to assess driving ability without interfering with driving.

FIG. 1 is a diagram for explaining a driving section and a driving load. 1 is a block diagram showing an example of a configuration of a main part of a driving ability determination system according to an embodiment of the present invention; FIG. 3 is a diagram for explaining fluctuations in the steering angle of a vehicle. FIG. 10 is a diagram illustrating an example of a degree display of the degree of steering shake. 3 is a flowchart showing an example of processing executed by a calculation unit in FIG. 2 .

Embodiments of the present invention will now be described with reference to Figures 1 to 5. A driving ability assessment system according to an embodiment of the present invention assesses the driving ability of a vehicle driver. Generally, a driver's driving behavior is composed of three elements: cognition, judgment, and operation. Of these elements, abilities related to "cognitive function," which is a person's intellectual function related to cognition and judgment, are known to gradually decline with age. Declining cognitive function makes it difficult to drive a vehicle safely. The system assesses driving ability related to cognitive function based on driving data when the driver drives a vehicle, allowing the driver and their family to understand trends in cognitive function decline and support safe driving.

Figure 1 is a diagram explaining driving sections and driving load. As shown in Figure 1, the driving load imposed on a driver due to driving behavior varies depending on the driving section, such as road shape. For example, driving load increases when driving on S-curves or winding roads, or when parking in a parking space. That is, the operational load due to driving operations increases in driving sections where the driver is required to make many steering maneuvers per unit of vehicle movement and the vehicle's driving trajectory has a complex shape. In this case, in addition to the high frequency of steering, the steering must be operated in coordination with accelerator and brake operations, and vehicle sense is also required, requiring high driving skills. In such driving sections (high-load sections), the driver's driving skills have a significant impact on driving stability.

On the other hand, there is almost no driving load when driving on straight roads. In other words, there is almost no steering required from the driver per unit of vehicle movement, and on driving sections where the vehicle's driving trajectory is extremely simple, there is almost no operational load due to driving operations. On such driving sections (no-load sections), the driver's driving skill has almost no effect on driving stability.

Driving on curved roads, changing lanes on multi-lane roads, turning right or left at intersections, etc., are driving loads that are somewhere in between these two. Even in such driving sections (low load sections), the driver's driving skill does not have much of an impact on driving stability.

However, even in low-load sections, when making a turn at an intersection to change the vehicle's direction of travel across the oncoming lane (a right turn in countries and regions where vehicles drive on the left, or a left turn in countries and regions where vehicles drive on the right; hereafter simply referred to as a "right turn"), the driver needs to understand the situation in the oncoming lane ahead while also understanding the situation in the lane ahead after the right turn in order to recognize the vehicle's target trajectory, resulting in a shift of gaze between the oncoming lane ahead and the lane ahead after the right turn. In such right-turn sections, the driver's mental activity increases, increasing driving load, particularly cognitive load related to cognition, and the state of the driver's cognitive function affects driving stability.

By acquiring driving data for such right-turn sections in a manner that allows it to be distinguished from other sections and evaluating driving stability based on that driving data, it is possible to determine the driver's driving ability related to cognitive function. Driving data for right-turn sections can be distinguished from other driving data based on steering wheel angle information.

However, even in low-load or no-load sections other than right-turn sections, cognitive load can increase if a specific event that places a psychological burden on the driver occurs. For example, cognitive load increases when some kind of information is announced in the vehicle while driving, when a situation occurs in which the vehicle's safety devices are activated, or when a situation occurs in which sudden braking or the horn is required. Therefore, in this embodiment, the driving ability assessment system is configured as follows to appropriately assess driving ability related to cognitive function by treating driving data when a specific event occurs separately from other driving data.

Figure 2 is a block diagram showing an example of the main configuration of a driving ability assessment system (hereinafter referred to as the system) 10. As shown in Figure 2, the system 10 is configured to include a computer having a calculation unit 11 such as a CPU, a storage unit 12 such as ROM and RAM, and peripheral circuits. The calculation unit 11 has, as its functional components, an information acquisition unit 13, a driving data extraction unit 14, an event occurrence determination unit 15, an evaluation value calculation unit 16, a cognitive function assessment unit 17, and an information output unit 18. The storage unit 12 stores information such as programs executed by the calculation unit 11 and setting values. The system 10 may be configured as an on-board device installed in a vehicle, or as a server device installed outside the vehicle.

The information acquisition unit 13 acquires time-series vehicle driving data for each pre-registered driver. For example, it acquires driving data measured for pre-registered vehicles driven daily by each driver. The driving data includes time-series information on the steering wheel angle, as well as information on the illumination of warning lights and indicator lights, activation information for safety devices such as electronic stability control and anti-lock braking systems, horn activation information, and vehicle deceleration information. It may also include information on the activation of warnings and functions from advanced driver assistance systems such as lane departure warnings. It may also include information on whether or not an alarm is output via an in-vehicle speaker or display, and whether or not a sound input above a predetermined volume is heard via an in-vehicle microphone from a nearby vehicle's horn, an emergency vehicle, a public address vehicle, etc. It may also include images of the driver's face taken by an in-vehicle camera and the results of image processing of those images, and images of the outside world taken by an exterior camera and the results of image processing of those images.

The driving data is transmitted to the system 10, for example at a predetermined interval, via a TCU (telematics control unit) installed in the vehicle. The information acquisition unit 13 acquires the driving data transmitted from pre-registered vehicles as time-series driving data for each pre-registered driver. The time-series driving data for each driver acquired by the information acquisition unit 13 is stored in the memory unit 12.

Based on the time-series driving data acquired by the information acquisition unit 13, the driving data extraction unit 14 extracts first driving data when the vehicle is driving in a no-load section or a low-load section (no-load/low-load section), and extracts second driving data when the vehicle is driving in a right-turn section. More specifically, the driving section is determined per unit time based on the change in steering angle over time, and driving data for the period when it is determined that the vehicle is driving in a no-load/low-load section is extracted as the first driving data. In addition, driving data for the period when it is determined that the vehicle is driving in a right-turn section is extracted as the second driving data.

The event occurrence determination unit 15 determines whether a predetermined event that increases cognitive load has occurred for each unit time based on the driving data acquired by the information acquisition unit 13, more specifically, the first driving data and the second driving data extracted by the driving data extraction unit 14. Here, the predetermined event may be a sudden event that occurs while the vehicle is driving and that is relatively difficult for the driver to foresee.

The event occurrence determination unit 15 determines whether a warning light or indicator light has been illuminated, for example, based on illumination information for the warning light or indicator light. It also determines whether a safety device has been activated based on safety device activation information, and whether the horn has been activated based on horn activation information. It also determines whether the vehicle's deceleration has increased to or above a predetermined value, i.e., whether the vehicle has been suddenly braked, based on deceleration information. It also determines whether a warning or function has been activated by an advanced driver assistance system, an in-vehicle notification has been output, or an audio input of a volume above a predetermined volume (for example, a horn from a nearby vehicle) has occurred.

In addition to or instead of these, the event occurrence determination unit 15 may determine whether a traffic participant is within a predetermined distance from the vehicle and whether the driver's line of sight is directed toward that traffic participant, based on images from an in-vehicle camera or an exterior camera and the results of image processing. It may also estimate the driver's emotion by matching the driver's facial expression to one of several human emotion patterns based on images from an in-vehicle camera and the results of image processing, and determine whether the estimated emotion is surprise.

The evaluation value calculation unit 16 calculates an α value (first evaluation value) representing the driver's steering characteristics based on the first driving data extracted by the driving data extraction unit 14, and calculates an Hp value (second evaluation value) representing the driver's steering characteristics when cognitive load increases based on the second driving data. At this time, the α value and Hp value are calculated after excluding or correcting, as described below, the driving data from the time when the event occurrence determination unit 15 determines that a predetermined event has occurred until a predetermined time has elapsed. In other words, since the occurrence of a predetermined event represents a special situation in which cognitive load increases abnormally, the driving data during that time is excluded or corrected. The predetermined time may be a fixed period of time (e.g., approximately 30 seconds) or may be changed depending on the nature of the event that occurred.

Figure 3 is a diagram explaining fluctuations in the steering angle θ of a vehicle. When the vehicle is being driven stably, steering is performed smoothly without any wobble, and fluctuations in the steering angle θ are small. On the other hand, when the vehicle is being driven unstable, steering becomes shaky and fluctuations in the steering angle θ become large.

More specifically, as shown in Figure 3, a predicted steering angle θp(n) at time n is calculated by second-order Taylor expansion centered on time (n-1) based on the actual steering angles θ(n-3), θ(n-2), and θ(n-1) at time points n-3, n-2, and n-1 immediately preceding time point n. Since the predicted steering angle θp(n) is an estimated value assuming smooth steering, if the actual steering is smooth, it will match the actual steering angle θ(n), but if the actual steering is unstable, it will deviate from the actual steering angle θ(n) depending on the degree of the unstable steering. The degree of such unstable steering can be expressed as a prediction error e(n) calculated by the following equation (i):
e(n)=θ(n)-θp(n)...(i)

When correcting and using driving data, the evaluation value calculation unit 16 corrects the driving data from the time it is determined that a specified event has occurred until a specified time has elapsed so that the prediction error e(n), which represents the degree of blur, is reduced. If a notification accompanied by steering wheel vibration is issued, the driving data during the notification is excluded, and the driving data for a specified period of time after the notification accompanied by vibration has ended is corrected.

Figure 4 is a diagram illustrating an example of a frequency display of the degree of steering wobble, showing an example of a frequency display of the prediction error e(n). The evaluation value calculation unit 16 calculates the predicted steering angle θp(n) and prediction error e(n) for each time point n based on the first driving data, which excludes and corrects driving data after the occurrence of a specified event, and calculates the 90th percentile value (α value) of the frequency distribution of the prediction error e(n) as shown by the solid line. The smoother the steering and the less steering wobble there is, the sharper the frequency distribution of the prediction error e(n) will be centered around "0°," where there is no steering wobble, and the smaller the α value will be. On the other hand, the more steering wobble there is, the broader the frequency distribution of the prediction error e(n) will be and the larger the α value will be.

By using the first driving data from the no-load/low-load section, which excludes the high-load section where there is a lot of steering and where driving skill has a large impact on steering error, it is possible to properly calculate the α value, which represents the driver's steering error under normal conditions. Furthermore, by excluding or correcting the driving data from the first section after the occurrence of a specified event, it is possible to more properly calculate the α value.

Furthermore, the evaluation value calculation unit 16 calculates an Hp value representing the driver's steering characteristics when cognitive load increases based on the calculated α value and the second driving data.

More specifically, the predicted steering angle θp(n) and prediction error e(n) at each time point n are calculated based on second driving data obtained by excluding and correcting driving data after the occurrence of a predetermined event, and the frequency distribution of the prediction error e(n) as shown by the dashed lines is divided into nine ranges P1 to P9 based on the α value. That is, based on eight reference values, −5α, −2.5α, −α, −0.5α, 0.5α, α, 2.5α, and 5α, the nine ranges are P1 (to −5α), P2 (−5α to −2.5α), P3 (−2.5α to −α), P4 (−α to −0.5α), P5 (−0.5α to 0.5α), P6 (0.5α to α), P7 (α to 2.5α), P8 (2.5α to 5α), and P9 (5α or higher). Then, based on the proportions p1 to p9 of the ranges P1 to P9, the steering entropy value (Hp value) is calculated using the following formula (ii).
Hp=-Σpi・log9pi...(ii)

The Hp value represents the smoothness of steering, and the smaller the steering error and the sharper the frequency distribution of the prediction error e(n), the smaller the value. The larger the steering error and the broader the frequency distribution of the prediction error e(n), the larger the value. By using the second driving data from the right-turn section, where there is a lot of eye movement and the cognitive function is significantly affected by steering error, it is possible to appropriately calculate the Hp value, which represents the driver's steering error when cognitive load is higher than normal. Furthermore, by excluding or correcting the driving data from the second section after a specified event has occurred, the Hp value can be more appropriately calculated.

The cognitive function evaluation unit 17 evaluates the driver's cognitive function based on the Hp value calculated by the evaluation value calculation unit 16. In other words, by continuously monitoring the Hp value, which represents steering fluctuations when cognitive load increases, it is possible to evaluate the driver's tendency toward decline in cognitive function. For example, if the Hp value, calculated periodically (e.g., monthly) based on driving data from daily driving, is on the rise, it is evaluated that the driver's cognitive function is on the decline.

The information output unit 18 transmits the evaluation results from the cognitive function evaluation unit 17 to a user device such as the driver or a family member. For example, a notification can be sent to a pre-registered email address. In this case, the notification can prompt the driver or a family member to consider returning their driver's license or switching to a vehicle with enhanced driving assistance functions. Because objective information based on driving data is provided, it is easier for the driver to accept the current state of their own cognitive function and consider appropriate measures early on.

Figure 5 is a flowchart showing an example of processing executed by the calculation unit 11 of the system 10. The processing shown in this flowchart is executed, for example, periodically. First, in step S1, all time-series driving data stored in the memory unit 12 is read. Next, in step S2, the driving section for each unit time is determined. Next, in step S3, from all the driving data read in step S1, first driving data for the period determined in step S2 to be a no-load/low-load section and second driving data for the period determined to be a right-turn section are extracted. Next, in step S4, based on the first driving data and second driving data extracted in step S3, it is determined whether a specified event has occurred for each unit time.

Next, in step S5, the driving data from the time when it is determined in step S4 that the predetermined event has occurred until a predetermined time has elapsed is excluded or corrected, and the α value is calculated based on the first driving data extracted in step S3. Next, in step S6, the driving data from the time when it is determined in step S4 that the predetermined event has occurred until a predetermined time has elapsed is excluded or corrected, and the Hp value is calculated based on the second driving data extracted in step S3 and the α value calculated in step S5. The latest Hp value calculated in step S6 is stored and accumulated in the memory unit 12. Next, in step S7, the latest Hp value stored in the memory unit 12 is compared with past Hp values to determine the driver's driving ability related to cognitive function. Next, in step S8, the evaluation result of step S7 is sent to a pre-registered email address, and the process ends.

In this way, the α and Hp values, which are indicators for assessing a driver's driving ability, can be calculated based solely on daily driving data, allowing for assessment of driving ability without interfering with driving (steps S1-S6). Furthermore, by excluding or correcting driving data after the occurrence of a specific event as a special situation in which cognitive load is abnormally high, the α and Hp values can be calculated appropriately (steps S2-S6). Furthermore, the driver's cognitive function is automatically assessed based solely on daily driving data, and the assessment results are notified to the driver and their family, reducing the burden on family members living far away from the elderly driver (steps S1-S8).

According to this embodiment, the following effects can be achieved.
(1) The system 10 includes an information acquisition unit 13 that acquires time-series driving data of the vehicle, an evaluation value calculation unit 16 that calculates an evaluation value of the driving ability that represents the steering characteristics of the driver based on the driving data, and an event occurrence determination unit 15 that determines whether a predetermined event that increases cognitive load has occurred based on the driving data ( FIG. 2 ).

The evaluation value calculation unit 16 identifies, from the driving data, driving data occurring after the time when it is determined that a predetermined event has occurred as specific driving data, and calculates a driving ability evaluation value based on driving data from which the specific driving data has been excluded, or based on driving data that has been corrected so that the weight of the specific driving data on the driving ability evaluation value is lower than the weight of the driving ability evaluation value of the other driving data. This makes it possible to calculate the α value and Hp value, which serve as indicators for determining a driver's driving ability, based on everyday driving data, thereby making it possible to determine driving ability without interfering with driving.

Furthermore, by treating driving data after the occurrence of a specific event separately from other driving data, it is possible to appropriately assess driving ability related to cognitive function. For example, by excluding driving data during a special situation in which cognitive load was abnormally high, it is possible to appropriately calculate α and Hp values.

(2) The evaluation value calculation unit 16 identifies, from the driving data, driving data from the time when it is determined that a predetermined event has occurred until a predetermined time has elapsed as specific driving data. In other words, when a predetermined event that imposes psychological stress on the driver occurs, the cognitive load increases for a certain period of time thereafter, and therefore, by treating the driving data during that period separately from other driving data, it is possible to appropriately assess driving ability related to cognitive function.

(3) The evaluation value calculation unit 16 calculates an α value representing the driver's steering characteristics based on driving data obtained by excluding specific driving data from the first driving data, or based on driving data corrected so that the weight of the specific driving data included in the first driving data is lower than the weight of the driving ability evaluation value of the remaining first driving data, and calculates an Hp value representing the driver's steering characteristics when a specified load is applied to the driver based on the calculated α value and the second driving data. In other words, after the occurrence of a specified event, a special situation is assumed in which cognitive load is abnormally high, and the driving data during that period is corrected to reduce the prediction error e(n), which represents the degree of deviation, and is then used to calculate the α value and Hp value. This allows the α value and Hp value to be calculated appropriately.

(4) The evaluation value calculation unit 16 calculates the Hp value based on the α value and driving data obtained by excluding specific driving data from the second driving data, or based on the α value and driving data corrected so that the weighting of the driving ability evaluation value of the specific driving data included in the second driving data is lower than the weighting of the driving ability evaluation value of the other second driving data.

(5) The specified event is any one of the following: the activation of an alarm device that notifies the driver of information; the activation of a safety device installed in the vehicle; an increase in the vehicle's deceleration to a specified value or more; and the sound of a warning horn around the vehicle. In other words, if any information is announced inside the vehicle while driving, if an event occurs in which the vehicle's safety device is activated, or if an event occurs in which sudden braking or the sound of a warning horn occurs, the driver will be placed under psychological stress and their cognitive load will increase. By treating the driving data when such a specified event occurs separately from other driving data, it is possible to appropriately assess driving ability related to cognitive function.

In the above embodiment, an example was described in which the driving data extraction unit 14 extracts the first driving data and the second driving data by determining the driving section per unit time based on the change in steering angle over time. However, the driving data extraction unit is not limited to this. For example, the driving section per unit time may be determined based on the change in vehicle position information over time, or the driving section may be identified based on position information and map information.

In the above embodiment, an example was described in which the α value was calculated based on the first driving data when traveling through a no-load/low-load section, and the Hp value was calculated based on the second driving data when traveling through a right-turn section, as shown in Figure 1, etc., but the first and second sections are not limited to such sections. The first and second sections may have a relationship such that the second section imposes a higher cognitive load than the first section. For example, the first section may be any no-load/low-load section excluding the second section.

While the present invention has been described above as a driving ability assessment system, it can also be used as a driving ability assessment method. That is, the driving ability assessment method includes an information acquisition step S1 for acquiring time-series driving data of a vehicle; evaluation value calculation steps S5 and S6 for calculating a driving ability assessment value representing the driver's steering characteristics based on the driving data; and an event occurrence determination step S4 for determining whether a predetermined event that increases cognitive load has occurred based on the driving data (Figure 5). In the evaluation value calculation steps S5 and S6, driving data after the time when the predetermined event was determined to have occurred is identified as specific driving data, and a driving ability assessment value is calculated based on driving data from which the specific driving data has been excluded, or based on driving data that has been corrected so that the weight of the driving ability assessment value for the specific driving data is lower than the weight of the driving ability assessment value for the remaining driving data.

The above description is merely an example, and the present invention is not limited to the above-described embodiments and variations, as long as the features of the present invention are not impaired. It is also possible to combine one or more of the above embodiments and variations in any way, and it is also possible to combine variations together.

10 Driving ability assessment system (system), 11 Calculation unit, 12 Memory unit, 13 Information acquisition unit, 14 Driving data extraction unit, 15 Event occurrence assessment unit, 16 Evaluation value calculation unit, 17 Cognitive function assessment unit, 18 Information output unit

Claims (5)

an information acquisition unit that acquires time-series driving data of the vehicle;
an evaluation value calculation unit that calculates an evaluation value representing the steering characteristics of the driver of the vehicle based on the driving data acquired by the information acquisition unit;
a travel data extraction unit that extracts first travel data of a no-load/low-load section other than a high-load section where a driving load imposed on a driver of the vehicle due to a driving behavior is large, and extracts second travel data of a right-turn section, based on the travel data acquired by the information acquisition unit;
an event occurrence determination unit that determines whether a predetermined event that imposes a predetermined load on a driver of the vehicle has occurred based on the traveling data acquired by the information acquisition unit,
The evaluation value calculation unit identifies, from the driving data acquired by the information acquisition unit, driving data acquired by the information acquisition unit after the point at which the event occurrence determination unit determines that the specified event has occurred as specific driving data, and calculates a first evaluation value representing the steering characteristics of the driver of the vehicle based on driving data obtained by excluding the specific driving data from the first driving data, or based on driving data corrected so that the weight for the evaluation value of the specific driving data included in the first driving data is lower than the weight for the evaluation value of the other first driving data, and calculates a second evaluation value representing the steering characteristics of the driver when the specified load is applied to the driver of the vehicle based on the calculated first evaluation value and the second driving data. 2. The driving ability determination system according to claim 1,
A driving ability assessment system characterized in that the evaluation value calculation unit identifies, from the driving data acquired by the information acquisition unit, driving data from the time when the event occurrence determination unit determines that the specified event has occurred to the time when a specified time has elapsed as the specified driving data. 3. The driving ability determination system according to claim 1,
A driving ability assessment system characterized in that the evaluation value calculation unit calculates the second evaluation value based on the first evaluation value and driving data in which the specific driving data is excluded from the second driving data, or based on the first evaluation value and driving data corrected so that the weight of the specific driving data included in the second driving data for the evaluation value is lower than the weight of the other second driving data for the evaluation value. In the driving ability determination system according to any one of claims 1 to 3,
A driving ability assessment system characterized in that the specified event is one of the following: the activation of an alarm device that notifies the driver of information, the activation of a safety device installed in the vehicle, an increase in the deceleration of the vehicle to a specified value or more, and the sound of a warning horn around the vehicle. an information acquisition step of acquiring time-series driving data of the vehicle;
an evaluation value calculation step of calculating an evaluation value representing the steering characteristics of the driver of the vehicle based on the traveling data acquired in the information acquisition step;
a travel data extraction unit that extracts first travel data of a no-load/low-load section other than a high-load section where a driving load imposed on a driver of the vehicle due to a driving behavior is large, and extracts second travel data of a right-turn section, based on the travel data acquired in the information acquisition step;
an event occurrence determination step of determining whether or not a predetermined event that imposes a predetermined load on the driver of the vehicle has occurred based on the traveling data acquired in the information acquisition step,
In the evaluation value calculation step, of the driving data acquired in the information acquisition step, driving data acquired in the information acquisition step after the point at which it is determined that the specified event has occurred in the event occurrence determination step is identified as specific driving data, and a first evaluation value representing the steering characteristics of the driver of the vehicle is calculated based on driving data obtained by excluding the specific driving data from the first driving data, or based on driving data corrected so that the weight for the evaluation value of the specific driving data included in the first driving data is lower than the weight for the evaluation value of the other first driving data, and a second evaluation value representing the steering characteristics of the driver when the specified load is applied to the driver of the vehicle is calculated based on the calculated first evaluation value and the second driving data.